Patch-based crack detection in black box road images using deep learning

Somin Park, Seongdeok Bang, Hongjo Kim, Hyoungkwan Kim

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

This paper proposes a method for patch-based crack detection of black box road images, for efficient road pavement monitoring. The proposed method is based on deep learning and consists of two modules: road extraction and crack detection. The road extraction module uses the segmentation process of a Fully Convolutional Network (FCN) called FCN-8s to leave only the road area in the image. The crack detection module performs patch-based crack detection on the extracted road area using a convolutional neural network. To the best of the authors’ knowledge, the proposed method is the first attempt to detect road cracks of black box images, which are not orthogonal but skewed actual road images.

Original languageEnglish
Publication statusPublished - 2018 Jan 1
Event35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018 - Berlin, Germany
Duration: 2018 Jul 202018 Jul 25

Other

Other35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018
CountryGermany
CityBerlin
Period18/7/2018/7/25

Fingerprint

Crack detection
Pavements
Cracks
Neural networks
Deep learning
Monitoring

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Building and Construction

Cite this

Park, S., Bang, S., Kim, H., & Kim, H. (2018). Patch-based crack detection in black box road images using deep learning. Paper presented at 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018, Berlin, Germany.
Park, Somin ; Bang, Seongdeok ; Kim, Hongjo ; Kim, Hyoungkwan. / Patch-based crack detection in black box road images using deep learning. Paper presented at 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018, Berlin, Germany.
@conference{7d995dfe633c4dd38d715186a0db58c3,
title = "Patch-based crack detection in black box road images using deep learning",
abstract = "This paper proposes a method for patch-based crack detection of black box road images, for efficient road pavement monitoring. The proposed method is based on deep learning and consists of two modules: road extraction and crack detection. The road extraction module uses the segmentation process of a Fully Convolutional Network (FCN) called FCN-8s to leave only the road area in the image. The crack detection module performs patch-based crack detection on the extracted road area using a convolutional neural network. To the best of the authors’ knowledge, the proposed method is the first attempt to detect road cracks of black box images, which are not orthogonal but skewed actual road images.",
author = "Somin Park and Seongdeok Bang and Hongjo Kim and Hyoungkwan Kim",
year = "2018",
month = "1",
day = "1",
language = "English",
note = "35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018 ; Conference date: 20-07-2018 Through 25-07-2018",

}

Park, S, Bang, S, Kim, H & Kim, H 2018, 'Patch-based crack detection in black box road images using deep learning' Paper presented at 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018, Berlin, Germany, 18/7/20 - 18/7/25, .

Patch-based crack detection in black box road images using deep learning. / Park, Somin; Bang, Seongdeok; Kim, Hongjo; Kim, Hyoungkwan.

2018. Paper presented at 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018, Berlin, Germany.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Patch-based crack detection in black box road images using deep learning

AU - Park, Somin

AU - Bang, Seongdeok

AU - Kim, Hongjo

AU - Kim, Hyoungkwan

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This paper proposes a method for patch-based crack detection of black box road images, for efficient road pavement monitoring. The proposed method is based on deep learning and consists of two modules: road extraction and crack detection. The road extraction module uses the segmentation process of a Fully Convolutional Network (FCN) called FCN-8s to leave only the road area in the image. The crack detection module performs patch-based crack detection on the extracted road area using a convolutional neural network. To the best of the authors’ knowledge, the proposed method is the first attempt to detect road cracks of black box images, which are not orthogonal but skewed actual road images.

AB - This paper proposes a method for patch-based crack detection of black box road images, for efficient road pavement monitoring. The proposed method is based on deep learning and consists of two modules: road extraction and crack detection. The road extraction module uses the segmentation process of a Fully Convolutional Network (FCN) called FCN-8s to leave only the road area in the image. The crack detection module performs patch-based crack detection on the extracted road area using a convolutional neural network. To the best of the authors’ knowledge, the proposed method is the first attempt to detect road cracks of black box images, which are not orthogonal but skewed actual road images.

UR - http://www.scopus.com/inward/record.url?scp=85053906223&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053906223&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85053906223

ER -

Park S, Bang S, Kim H, Kim H. Patch-based crack detection in black box road images using deep learning. 2018. Paper presented at 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018, Berlin, Germany.